Comparison of the frequency of loss‐of‐function LZTR1 variants between schwannomatosis patients and the general population

Abstract Schwannomatosis is a rare tumor predisposition syndrome that causes multiple schwannomas. Germline loss‐of‐function (LoF) LZTR1 variants were only recently identified as disease‐causing, so relatively few variants have been identified in patients. In addition, many LoF variants exist in Genome Aggregation Database (gnomAD) in people who do not have clinical symptoms of schwannomatosis. These factors, and the incomplete penetrance seen in this condition, hinder definitive interpretation of the clinical significance of novel LoF variants identified in schwannomatosis patients. We collated published LOF LZTR1 variants identified in schwannomatosis patients and classified them according to current American College of Medical Genetics and Genomics/Association for Molecular Pathology/Association of Clinical Genomic Science guidelines. Subsequently, pathogenic/likely pathogenic schwannomatosis‐associated LoF variants were compared with LoF LZTR1 variants reported in gnomAD data. Using current classification guidelines, 64/71 LoF LZTR1 variants reported in schwannomatosis patients in the literature were classified as pathogenic/likely pathogenic, and their frequency in probands 64/359 (17.8%) was significantly higher than the frequency of potential LoF variants identified in the general population (0.36%; p < 0.0001). The majority of published classifications of schwannomatosis‐associated LoF variants are robust. However, the high frequency of LoF LZTR1 variants in the general population suggests that LZTR1 variants confer a reduced risk of schwannomas compared to germline NF2 and SMARCB1 pathogenic variants, making classification of novel variants challenging.

The temperature scale for variants of uncertain significance suggests 0 points for an ice cold VUS, 1 point for a cold VUS, 2 points for a cool VUS, 3 points for a tepid VUS, 4 points for a warm VUS, and 5 points for a hot VUS (https://www.acgs.uk.com/quality/best-practiceguidelines/#VariantGuidelines).

Clinical data
When investigating the clinical data of the variant, the principal considerations involve whether the variant is de novo, whether the variant segregates with disease in families, and whether the phenotype of the patient is highly specific for schwannomatosis.
ACMG classifier PP4 was applied extensively, since all variants were identified in highly characterized patients meeting schwannomatosis clinical diagnostic criteria. PS2/PM6 were not used, as although many cases were listed as de novo, there was insufficient evidence to determine whether parents had been tested for the variant, and/or whether paternity/paternity had been confirmed.
PP1 was also not used, as there were an insufficient number of meioses to support this according to the Jarvik and Browning (2016) thresholds. We did not apply BS3 in cases where a variant was found in an unaffected individual, due to the known incomplete penetrance of disease in schwannomatosis and age-related appearance of symptoms. However, for the variant which was not seen in an affected relative, BS4 was applied.

Functional data and predictive data
Using recommendations suggested by Abou Tayoun et al., 2018, we assigned the ACMG classifier PVS1 for nonsense variants and frameshift variants that were not in the last exon of the gene, and which were predicted to cause nonsense mediated decay (NMD). Nonsense or frameshift variants in the last exon were assigned PVS1_M. Variants occurring at ±1 or 2 canonical splice-sites, except for the last exon and predicted to skip an out-of-frame exon were also classified as PVS1. For variants occurring at the ±1 or 2 canonical splice-site at the last exon or predicted to skip an in-frame exon, we assigned PVS1_M. For non-canonical splice variants, when RNA analysis proved that the variant caused an out-of-frame truncated transcript, PVS1 was applied. When RNA analysis proved that the variant caused an in-frame truncated transcript, PVS1_M was applied. There is not currently enough data on the effects of specific splice variants that disrupt or remove particular functional LZTR1 protein domains to use this information with confidence. Therefore our assessments are only based on the maintenance or disruption of the overall reading frame of the LZTR1 transcript.
Computational and predictive tools were used to perform in silico analysis for splice-site variants to determine the application of PP3 or BP4. For in silico predictions we used AlamutVisualPlus, which includes the NNSplice and GeneSplicer algorithms recommended by the ACGS guidelines and SpliceAI (https://spliceailookup.broadinstitute.org/), (Jaganathan et al., 2019) which has been shown to be one of the best performing meta-predictors. Only Splice AI prediction scores >0.5 (indicating a confident prediction of disruption to splicing) were considered to be supportive of splice changes. Alamut Visual Plus_1.4 reports were also used to assess aberrant splicing, as they include MaxEntScan and SpliceSiteFinder-Like predictions that are recommended by ACGS (Ellard et al. 2020), as well as NNSplice and GeneSplicer, suggested by ACMG (Richards et al. 2015). Where the results were inconclusive no classifier was used, where only in silico predictions were available PP3 was used, and the PS3 classifier was not applied in combination with any level of PVS1 classifier.

Variant population frequency
PM2 was applied if LZTR1 variants had not been reported in gnomAD. Due to the rarity of schwannomatosis disease and the known incomplete penetrance of disease, classifiers BS1 and PS4 were used with a reduced strength. If a variant was seen in 1-2 affected probands and controls, or 2 affected probands and >2 controls, then neither classifier was used. If a variant was seen once in the case cohort and >2 times in controls, then BS1 was used at a moderate level (BS1_M). If a variant was seen in >2 times in the case cohort and >2 times in controls, then PS4 was used at a moderate level (PS4_M).